Deep learning characterization of brain tumours with diffusion weighted imaging. (21st January 2023)

• Record Type: Journal Article
• Authors: Meaney, Cameron; Das, Sunit; Colak, Errol; Kohandel, Mohammad
• Is Part Of: Journal of theoretical biology. Volume 557(2023)
• Publication Date: 2023-01-21
• Subjects: Deep learning -- Cancer -- Medical imaging -- Applied mathematics -- Mathematical oncology -- Mathematical medicine -- PDEs -- Machine learning -- Neural networks; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2022.111342
• Languages: English
• ISSNs: 0022-5193

Abstract

Abstract: Glioblastoma multiforme (GBM) is one of the most deadly forms of cancer. Methods of characterizing these tumours are valuable for improving predictions of their progression and response to treatment. A mathematical model called the proliferation-invasion (PI) model has been used extensively in the literature to model the growth of these tumours, though it relies on known values of two key parameters: the tumour cell diffusivity and proliferation rate. Unfortunately, these parameters are difficult to estimate in a patient-specific manner, making personalized tumour forecasting challenging. In this paper, we develop and apply a deep learning model capable of making accurate estimates of these key GBM-characterizing parameters while simultaneously producing a full prediction of the tumour progression curve. Our method uses two sets of multi sequence MRI in order to produce estimations and relies on a preprocessing pipeline which includes brain tumour segmentation and conversion to tumour cellularity. We first apply our deep learning model to synthetic tumours to showcase the model's capabilities and identify situations where prediction errors are likely to occur. We then apply our model to a clinical dataset consisting of five patients diagnosed with GBM. For all patients, we derive evidence-based estimates for each of the PI model parameters and predictions for the future progression of the tumour, along with estimates of the parameter uncertainties. Our work provides a new, easily generalizable method for the estimation of patient-specific tumour parameters, which can be built upon to aid physicians in designing personalized treatments. Highlights: Mathematical models are used to predict tumour growth and response to treatment. Use of models in the clinic requires patient-specific tumour characterizations. Deep learning algorithms can use MRIs to characterize tumours and inform models. Deep learning characterization models show advantages over traditional methods.